Overview

Dataset statistics

Number of variables19
Number of observations1892
Missing cells5468
Missing cells (%)15.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory282.9 KiB
Average record size in memory153.1 B

Variable types

Numeric9
DateTime1
Categorical9

Alerts

rating_denominator has constant value "10" Constant
text has a high cardinality: 1892 distinct values High cardinality
expanded_urls has a high cardinality: 1778 distinct values High cardinality
name has a high cardinality: 812 distinct values High cardinality
t_link has a high cardinality: 1578 distinct values High cardinality
id is highly correlated with in_reply_to_status_id and 5 other fieldsHigh correlation
in_reply_to_status_id is highly correlated with id and 4 other fieldsHigh correlation
in_reply_to_user_id is highly correlated with id and 3 other fieldsHigh correlation
retweeted_status_id is highly correlated with id and 3 other fieldsHigh correlation
rating_numerator is highly correlated with idHigh correlation
retweet_count is highly correlated with id and 2 other fieldsHigh correlation
month is highly correlated with id and 5 other fieldsHigh correlation
day_of_the year is highly correlated with id and 5 other fieldsHigh correlation
year is highly correlated with id and 4 other fieldsHigh correlation
rating_denominator is highly correlated with year and 3 other fieldsHigh correlation
stage is highly correlated with in_reply_to_status_id and 1 other fieldsHigh correlation
favourites_count is highly correlated with rating_denominatorHigh correlation
source is highly correlated with rating_denominatorHigh correlation
in_reply_to_status_id has 1833 (96.9%) missing values Missing
in_reply_to_user_id has 1833 (96.9%) missing values Missing
retweeted_status_id has 1722 (91.0%) missing values Missing
expanded_urls has 43 (2.3%) missing values Missing
name has 37 (2.0%) missing values Missing
text is uniformly distributed Uniform
expanded_urls is uniformly distributed Uniform
id has unique values Unique
timestamp has unique values Unique
text has unique values Unique
week has 303 (16.0%) zeros Zeros

Reproduction

Analysis started2022-09-14 21:06:14.604472
Analysis finished2022-09-14 21:09:14.838806
Duration3 minutes and 0.23 seconds
Software versionpandas-profiling v3.3.0
Download configurationconfig.json

Variables

id
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct1892
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.549565161 × 1017
Minimum6.660507588 × 1017
Maximum8.924206436 × 1017
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size29.6 KiB
2022-09-15T00:09:15.199823image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum6.660507588 × 1017
5-th percentile6.6932792 × 1017
Q16.874723173 × 1017
median7.490698138 × 1017
Q38.145572518 × 1017
95-th percentile8.734468895 × 1017
Maximum8.924206436 × 1017
Range2.263698848 × 1017
Interquartile range (IQR)1.270849344 × 1017

Descriptive statistics

Standard deviation6.893761917 × 1016
Coefficient of variation (CV)0.09131336401
Kurtosis-1.230421654
Mean7.549565161 × 1017
Median Absolute Deviation (MAD)6.341984596 × 1016
Skewness0.3106799236
Sum7.978434787 × 1018
Variance4.752395336 × 1033
MonotonicityStrictly decreasing
2022-09-15T00:09:15.735831image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8.924206436 × 10171
 
0.1%
7.030419497 × 10171
 
0.1%
7.018056424 × 10171
 
0.1%
7.018891871 × 10171
 
0.1%
7.019528166 × 10171
 
0.1%
7.019813905 × 10171
 
0.1%
7.022767488 × 10171
 
0.1%
7.023211405 × 10171
 
0.1%
7.023325423 × 10171
 
0.1%
7.025395137 × 10171
 
0.1%
Other values (1882)1882
99.5%
ValueCountFrequency (%)
6.660507588 × 10171
0.1%
6.66055525 × 10171
0.1%
6.660638273 × 10171
0.1%
6.660731008 × 10171
0.1%
6.661021559 × 10171
0.1%
6.662689108 × 10171
0.1%
6.662730976 × 10171
0.1%
6.663454176 × 10171
0.1%
6.663737537 × 10171
0.1%
6.664187895 × 10171
0.1%
ValueCountFrequency (%)
8.924206436 × 10171
0.1%
8.921774213 × 10171
0.1%
8.918151814 × 10171
0.1%
8.916895573 × 10171
0.1%
8.913275589 × 10171
0.1%
8.910879509 × 10171
0.1%
8.909719132 × 10171
0.1%
8.907291814 × 10171
0.1%
8.906091852 × 10171
0.1%
8.902402553 × 10171
0.1%

in_reply_to_status_id
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct58
Distinct (%)98.3%
Missing1833
Missing (%)96.9%
Infinite0
Infinite (%)0.0%
Mean7.483041841 × 1017
Minimum6.658146967 × 1017
Maximum8.862663571 × 1017
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size29.6 KiB
2022-09-15T00:09:16.256808image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum6.658146967 × 1017
5-th percentile6.671521641 × 1017
Q16.75174596 × 1017
median7.291135313 × 1017
Q38.22239491 × 1017
95-th percentile8.716089652 × 1017
Maximum8.862663571 × 1017
Range2.204516604 × 1017
Interquartile range (IQR)1.47064895 × 1017

Descriptive statistics

Standard deviation7.666742434 × 1016
Coefficient of variation (CV)0.102454892
Kurtosis-1.422470611
Mean7.483041841 × 1017
Median Absolute Deviation (MAD)5.755252913 × 1016
Skewness0.4408480782
Sum4.414994686 × 1019
Variance5.877893954 × 1033
MonotonicityNot monotonic
2022-09-15T00:09:16.734539image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.671521641 × 10172
 
0.1%
6.780211157 × 10171
 
0.1%
7.291135313 × 10171
 
0.1%
7.079800659 × 10171
 
0.1%
7.032559358 × 10171
 
0.1%
7.044857446 × 10171
 
0.1%
6.753493843 × 10171
 
0.1%
6.706683835 × 10171
 
0.1%
6.936422322 × 10171
 
0.1%
6.935722159 × 10171
 
0.1%
Other values (48)48
 
2.5%
(Missing)1833
96.9%
ValueCountFrequency (%)
6.658146967 × 10171
0.1%
6.670655356 × 10171
0.1%
6.671521641 × 10172
0.1%
6.678064546 × 10171
0.1%
6.689207171 × 10171
0.1%
6.693543826 × 10171
0.1%
6.706683835 × 10171
0.1%
6.715610021 × 10171
0.1%
6.717299066 × 10171
0.1%
6.744688809 × 10171
0.1%
ValueCountFrequency (%)
8.862663571 × 10171
0.1%
8.816070373 × 10171
0.1%
8.795538273 × 10171
0.1%
8.707262027 × 10171
0.1%
8.634256456 × 10171
0.1%
8.571566781 × 10171
0.1%
8.562860041 × 10171
0.1%
8.558181173 × 10171
0.1%
8.503288188 × 10171
0.1%
8.482121117 × 10171
0.1%

in_reply_to_user_id
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct27
Distinct (%)45.8%
Missing1833
Missing (%)96.9%
Infinite0
Infinite (%)0.0%
Mean2.662801758 × 1016
Minimum11856342
Maximum8.405478644 × 1017
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size29.6 KiB
2022-09-15T00:09:17.139738image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum11856342
5-th percentile16476451.8
Q1325415888.5
median4196983835
Q34196983835
95-th percentile4249015199
Maximum8.405478644 × 1017
Range8.405478643 × 1017
Interquartile range (IQR)3871567946

Descriptive statistics

Standard deviation1.437385234 × 1017
Coefficient of variation (CV)5.398018195
Kurtosis27.4932333
Mean2.662801758 × 1016
Median Absolute Deviation (MAD)0
Skewness5.329408792
Sum1.571053037 × 1018
Variance2.06607631 × 1034
MonotonicityNot monotonic
2022-09-15T00:09:17.531671image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
419698383533
 
1.7%
118563421
 
0.1%
163746781
 
0.1%
4670367061
 
0.1%
11989885101
 
0.1%
287854861
 
0.1%
23191081981
 
0.1%
3589727681
 
0.1%
47172974761
 
0.1%
158464071
 
0.1%
Other values (17)17
 
0.9%
(Missing)1833
96.9%
ValueCountFrequency (%)
118563421
0.1%
158464071
0.1%
163746781
0.1%
164877601
0.1%
206837241
0.1%
214356581
0.1%
219550581
0.1%
287854861
0.1%
291663051
0.1%
473844301
0.1%
ValueCountFrequency (%)
8.405478644 × 10171
 
0.1%
7.305050142 × 10171
 
0.1%
47172974761
 
0.1%
419698383533
1.7%
31054407461
 
0.1%
28941311801
 
0.1%
23191081981
 
0.1%
22811816001
 
0.1%
15828538091
 
0.1%
11989885101
 
0.1%

timestamp
Date

UNIQUE

Distinct1892
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size29.6 KiB
Minimum2015-11-16 00:30:50+00:00
Maximum2017-08-01 16:23:56+00:00
2022-09-15T00:09:18.007981image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-15T00:09:18.510950image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

source
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size29.6 KiB
iPhone
1778 
Vine
 
84
Web Client
 
21
tweetdeck
 
9

Length

Max length10
Median length6
Mean length5.96987315
Min length4

Characters and Unicode

Total characters11295
Distinct characters17
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowiPhone
2nd rowiPhone
3rd rowiPhone
4th rowiPhone
5th rowiPhone

Common Values

ValueCountFrequency (%)
iPhone1778
94.0%
Vine84
 
4.4%
Web Client21
 
1.1%
tweetdeck9
 
0.5%

Length

2022-09-15T00:09:18.997326image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-15T00:09:19.419775image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
iphone1778
92.9%
vine84
 
4.4%
web21
 
1.1%
client21
 
1.1%
tweetdeck9
 
0.5%

Most occurring characters

ValueCountFrequency (%)
e1931
17.1%
i1883
16.7%
n1883
16.7%
h1778
15.7%
o1778
15.7%
P1778
15.7%
V84
 
0.7%
t39
 
0.3%
C21
 
0.2%
l21
 
0.2%
Other values (7)99
 
0.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter9370
83.0%
Uppercase Letter1904
 
16.9%
Space Separator21
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e1931
20.6%
i1883
20.1%
n1883
20.1%
h1778
19.0%
o1778
19.0%
t39
 
0.4%
l21
 
0.2%
b21
 
0.2%
w9
 
0.1%
d9
 
0.1%
Other values (2)18
 
0.2%
Uppercase Letter
ValueCountFrequency (%)
P1778
93.4%
V84
 
4.4%
C21
 
1.1%
W21
 
1.1%
Space Separator
ValueCountFrequency (%)
21
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin11274
99.8%
Common21
 
0.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
e1931
17.1%
i1883
16.7%
n1883
16.7%
h1778
15.8%
o1778
15.8%
P1778
15.8%
V84
 
0.7%
t39
 
0.3%
C21
 
0.2%
l21
 
0.2%
Other values (6)78
 
0.7%
Common
ValueCountFrequency (%)
21
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII11295
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e1931
17.1%
i1883
16.7%
n1883
16.7%
h1778
15.7%
o1778
15.7%
P1778
15.7%
V84
 
0.7%
t39
 
0.3%
C21
 
0.2%
l21
 
0.2%
Other values (7)99
 
0.9%

text
Categorical

HIGH CARDINALITY
UNIFORM
UNIQUE

Distinct1892
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size29.6 KiB
This is Phineas. He's a mystical boy. Only ever appears in the hole of a donut. 13/10 https://t.co/MgUWQ76dJU
 
1
This is an East African Chalupa Seal. We only rate dogs. Please only send in dogs. Thank you... 10/10 https://t.co/iHe6liLwWR
 
1
Please pray for this pupper. Nothing wrong with her she just can't stop getting hit with banana peels. 11/10 https://t.co/8sdVenUAqr
 
1
This is Bilbo. He's not emotionally prepared to enter the water. 11/10 don't struggle Bilbo https://t.co/rH9SQgZUnQ
 
1
Meet Rilo. He's a Northern Curly Ticonderoga. Currently balancing on one paw even in strong wind. Acrobatic af 11/10 https://t.co/KInss2PXyX
 
1
Other values (1887)
1887 

Length

Max length167
Median length142
Mean length119.2209302
Min length11

Characters and Unicode

Total characters225566
Distinct characters111
Distinct categories14 ?
Distinct scripts3 ?
Distinct blocks8 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1892 ?
Unique (%)100.0%

Sample

1st rowThis is Phineas. He's a mystical boy. Only ever appears in the hole of a donut. 13/10 https://t.co/MgUWQ76dJU
2nd rowThis is Tilly. She's just checking pup on you. Hopes you're doing ok. If not, she's available for pats, snugs, boops, the whole bit. 13/10 https://t.co/0Xxu71qeIV
3rd rowThis is Archie. He is a rare Norwegian Pouncing Corgo. Lives in the tall grass. You never know when one may strike. 12/10 https://t.co/wUnZnhtVJB
4th rowThis is Darla. She commenced a snooze mid meal. 13/10 happens to the best of us https://t.co/tD36da7qLQ
5th rowThis is Franklin. He would like you to stop calling him "cute." He is a very fierce shark and should be respected as such. 12/10 #BarkWeek https://t.co/AtUZn91f7f

Common Values

ValueCountFrequency (%)
This is Phineas. He's a mystical boy. Only ever appears in the hole of a donut. 13/10 https://t.co/MgUWQ76dJU1
 
0.1%
This is an East African Chalupa Seal. We only rate dogs. Please only send in dogs. Thank you... 10/10 https://t.co/iHe6liLwWR1
 
0.1%
Please pray for this pupper. Nothing wrong with her she just can't stop getting hit with banana peels. 11/10 https://t.co/8sdVenUAqr1
 
0.1%
This is Bilbo. He's not emotionally prepared to enter the water. 11/10 don't struggle Bilbo https://t.co/rH9SQgZUnQ1
 
0.1%
Meet Rilo. He's a Northern Curly Ticonderoga. Currently balancing on one paw even in strong wind. Acrobatic af 11/10 https://t.co/KInss2PXyX1
 
0.1%
This is Fiji. She's a Powdered Stegafloof. Very rare. 12/10 https://t.co/fZRob6eotY1
 
0.1%
"AND IIIIIIIIIIIEIIIIIIIIIIIII WILL ALWAYS LOVE YOUUUUU" 11/10 https://t.co/rSNCEiTtfI1
 
0.1%
Please enjoy this picture as much as I did. 12/10 https://t.co/7u8mM99Tj51
 
0.1%
This is Rudy. He's going to be a star. 13/10 talented af (vid by @madalynrossi) https://t.co/Dph4FDGoMd1
 
0.1%
This is a Wild Tuscan Poofwiggle. Careful not to startle. Rare tongue slip. One eye magical. 12/10 would def pet https://t.co/4EnShAQjv61
 
0.1%
Other values (1882)1882
99.5%

Length

2022-09-15T00:09:19.745004image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
is1312
 
3.8%
this1289
 
3.8%
a946
 
2.8%
to625
 
1.8%
the621
 
1.8%
12/10557
 
1.6%
he540
 
1.6%
11/10462
 
1.3%
10/10457
 
1.3%
he's455
 
1.3%
Other values (6975)27002
78.8%

Most occurring characters

ValueCountFrequency (%)
32312
 
14.3%
e15276
 
6.8%
t15164
 
6.7%
s12321
 
5.5%
o12064
 
5.3%
a9827
 
4.4%
i9538
 
4.2%
h8669
 
3.8%
/7443
 
3.3%
n7268
 
3.2%
Other values (101)95684
42.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter146082
64.8%
Space Separator32312
 
14.3%
Other Punctuation18398
 
8.2%
Uppercase Letter17367
 
7.7%
Decimal Number10692
 
4.7%
Connector Punctuation199
 
0.1%
Control151
 
0.1%
Close Punctuation122
 
0.1%
Open Punctuation122
 
0.1%
Dash Punctuation74
 
< 0.1%
Other values (4)47
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e15276
 
10.5%
t15164
 
10.4%
s12321
 
8.4%
o12064
 
8.3%
a9827
 
6.7%
i9538
 
6.5%
h8669
 
5.9%
n7268
 
5.0%
r7214
 
4.9%
l6432
 
4.4%
Other values (23)42309
29.0%
Uppercase Letter
ValueCountFrequency (%)
T2045
 
11.8%
H1587
 
9.1%
S1172
 
6.7%
I846
 
4.9%
A766
 
4.4%
M752
 
4.3%
R736
 
4.2%
B696
 
4.0%
C693
 
4.0%
P656
 
3.8%
Other values (16)7418
42.7%
Other Symbol
ValueCountFrequency (%)
🐶10
26.3%
🎶6
15.8%
😂3
 
7.9%
👏3
 
7.9%
🇸2
 
5.3%
🇺2
 
5.3%
😢1
 
2.6%
1
 
2.6%
🍻1
 
2.6%
🎉1
 
2.6%
Other values (8)8
21.1%
Other Punctuation
ValueCountFrequency (%)
/7443
40.5%
.6578
35.8%
:2056
 
11.2%
'1296
 
7.0%
@277
 
1.5%
*197
 
1.1%
,169
 
0.9%
"140
 
0.8%
76
 
0.4%
;44
 
0.2%
Other values (5)122
 
0.7%
Decimal Number
ValueCountFrequency (%)
14592
42.9%
02703
25.3%
2854
 
8.0%
3634
 
5.9%
4362
 
3.4%
7329
 
3.1%
6325
 
3.0%
5315
 
2.9%
8289
 
2.7%
9289
 
2.7%
Space Separator
ValueCountFrequency (%)
32312
100.0%
Connector Punctuation
ValueCountFrequency (%)
_199
100.0%
Control
ValueCountFrequency (%)
151
100.0%
Close Punctuation
ValueCountFrequency (%)
)122
100.0%
Open Punctuation
ValueCountFrequency (%)
(122
100.0%
Dash Punctuation
ValueCountFrequency (%)
-74
100.0%
Math Symbol
ValueCountFrequency (%)
=4
100.0%
Modifier Symbol
ValueCountFrequency (%)
🏻3
100.0%
Nonspacing Mark
ValueCountFrequency (%)
2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin163449
72.5%
Common62115
 
27.5%
Inherited2
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e15276
 
9.3%
t15164
 
9.3%
s12321
 
7.5%
o12064
 
7.4%
a9827
 
6.0%
i9538
 
5.8%
h8669
 
5.3%
n7268
 
4.4%
r7214
 
4.4%
l6432
 
3.9%
Other values (49)59676
36.5%
Common
ValueCountFrequency (%)
32312
52.0%
/7443
 
12.0%
.6578
 
10.6%
14592
 
7.4%
02703
 
4.4%
:2056
 
3.3%
'1296
 
2.1%
2854
 
1.4%
3634
 
1.0%
4362
 
0.6%
Other values (41)3285
 
5.3%
Inherited
ValueCountFrequency (%)
2
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII225436
99.9%
Punctuation76
 
< 0.1%
None40
 
< 0.1%
Emoticons5
 
< 0.1%
Enclosed Alphanum Sup4
 
< 0.1%
VS2
 
< 0.1%
Misc Symbols2
 
< 0.1%
Dingbats1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
32312
 
14.3%
e15276
 
6.8%
t15164
 
6.7%
s12321
 
5.5%
o12064
 
5.4%
a9827
 
4.4%
i9538
 
4.2%
h8669
 
3.8%
/7443
 
3.3%
n7268
 
3.2%
Other values (73)95554
42.4%
Punctuation
ValueCountFrequency (%)
76
100.0%
None
ValueCountFrequency (%)
🐶10
25.0%
🎶6
15.0%
é4
 
10.0%
👏3
 
7.5%
🏻3
 
7.5%
á2
 
5.0%
ó1
 
2.5%
ñ1
 
2.5%
ä1
 
2.5%
🍻1
 
2.5%
Other values (8)8
20.0%
Emoticons
ValueCountFrequency (%)
😂3
60.0%
😢1
 
20.0%
😉1
 
20.0%
Enclosed Alphanum Sup
ValueCountFrequency (%)
🇸2
50.0%
🇺2
50.0%
VS
ValueCountFrequency (%)
2
100.0%
Dingbats
ValueCountFrequency (%)
1
100.0%
Misc Symbols
ValueCountFrequency (%)
1
50.0%
1
50.0%

retweeted_status_id
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct170
Distinct (%)100.0%
Missing1722
Missing (%)91.0%
Infinite0
Infinite (%)0.0%
Mean7.749665736 × 1017
Minimum6.671382697 × 1017
Maximum8.860537344 × 1017
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size29.6 KiB
2022-09-15T00:09:20.072830image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum6.671382697 × 1017
5-th percentile6.710982898 × 1017
Q17.33930252 × 1017
median7.825142333 × 1017
Q38.218710416 × 1017
95-th percentile8.663986222 × 1017
Maximum8.860537344 × 1017
Range2.189154647 × 1017
Interquartile range (IQR)8.794078964 × 1016

Descriptive statistics

Standard deviation6.111924085 × 1016
Coefficient of variation (CV)0.07886693818
Kurtosis-0.9451326486
Mean7.749665736 × 1017
Median Absolute Deviation (MAD)4.312311472 × 1016
Skewness-0.2702657982
Sum1.317443175 × 1020
Variance3.735561602 × 1033
MonotonicityNot monotonic
2022-09-15T00:09:20.379937image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.961497491 × 10171
 
0.1%
6.75501076 × 10171
 
0.1%
6.718968093 × 10171
 
0.1%
6.704449557 × 10171
 
0.1%
6.67509364 × 10171
 
0.1%
6.671827921 × 10171
 
0.1%
7.717704565 × 10171
 
0.1%
6.873173063 × 10171
 
0.1%
7.809316142 × 10171
 
0.1%
7.89530877 × 10171
 
0.1%
Other values (160)160
 
8.5%
(Missing)1722
91.0%
ValueCountFrequency (%)
6.671382697 × 10171
0.1%
6.671521641 × 10171
0.1%
6.671827921 × 10171
0.1%
6.67509364 × 10171
0.1%
6.675486957 × 10171
0.1%
6.678667243 × 10171
0.1%
6.690003974 × 10171
0.1%
6.703191306 × 10171
0.1%
6.704449557 × 10171
0.1%
6.718968093 × 10171
0.1%
ValueCountFrequency (%)
8.860537344 × 10171
0.1%
8.78281511 × 10171
0.1%
8.78057613 × 10171
0.1%
8.768507723 × 10171
0.1%
8.732137756 × 10171
0.1%
8.726575843 × 10171
0.1%
8.688803978 × 10171
0.1%
8.685522785 × 10171
0.1%
8.664507055 × 10171
0.1%
8.663349648 × 10171
0.1%

expanded_urls
Categorical

HIGH CARDINALITY
MISSING
UNIFORM

Distinct1778
Distinct (%)96.2%
Missing43
Missing (%)2.3%
Memory size29.6 KiB
https://twitter.com/dog_rates/status/819227688460238848/photo/1
 
2
https://www.gofundme.com/my-puppys-double-cataract-surgery,https://twitter.com/dog_rates/status/825026590719483904/photo/1,https://twitter.com/dog_rates/status/825026590719483904/photo/1
 
2
https://www.gofundme.com/help-lorenzo-beat-cancer,https://twitter.com/dog_rates/status/860563773140209665/photo/1,https://twitter.com/dog_rates/status/860563773140209665/photo/1
 
2
https://twitter.com/dog_rates/status/762464539388485633/photo/1,https://twitter.com/dog_rates/status/762464539388485633/photo/1,https://twitter.com/dog_rates/status/762464539388485633/photo/1,https://twitter.com/dog_rates/status/762464539388485633/photo/1
 
2
https://twitter.com/dog_rates/status/762699858130116608/photo/1
 
2
Other values (1773)
1839 

Length

Max length511
Median length63
Mean length96.95457004
Min length29

Characters and Unicode

Total characters179269
Distinct characters72
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1707 ?
Unique (%)92.3%

Sample

1st rowhttps://twitter.com/dog_rates/status/892420643555336193/photo/1
2nd rowhttps://twitter.com/dog_rates/status/892177421306343426/photo/1
3rd rowhttps://twitter.com/dog_rates/status/891815181378084864/photo/1
4th rowhttps://twitter.com/dog_rates/status/891689557279858688/photo/1
5th rowhttps://twitter.com/dog_rates/status/891327558926688256/photo/1,https://twitter.com/dog_rates/status/891327558926688256/photo/1

Common Values

ValueCountFrequency (%)
https://twitter.com/dog_rates/status/819227688460238848/photo/12
 
0.1%
https://www.gofundme.com/my-puppys-double-cataract-surgery,https://twitter.com/dog_rates/status/825026590719483904/photo/1,https://twitter.com/dog_rates/status/825026590719483904/photo/12
 
0.1%
https://www.gofundme.com/help-lorenzo-beat-cancer,https://twitter.com/dog_rates/status/860563773140209665/photo/1,https://twitter.com/dog_rates/status/860563773140209665/photo/12
 
0.1%
https://twitter.com/dog_rates/status/762464539388485633/photo/1,https://twitter.com/dog_rates/status/762464539388485633/photo/1,https://twitter.com/dog_rates/status/762464539388485633/photo/1,https://twitter.com/dog_rates/status/762464539388485633/photo/12
 
0.1%
https://twitter.com/dog_rates/status/762699858130116608/photo/12
 
0.1%
https://www.gofundme.com/helpquinny,https://twitter.com/dog_rates/status/863062471531167744/photo/1,https://twitter.com/dog_rates/status/863062471531167744/photo/1,https://twitter.com/dog_rates/status/863062471531167744/photo/1,https://twitter.com/dog_rates/status/863062471531167744/photo/12
 
0.1%
https://twitter.com/dog_rates/status/820749716845686786/photo/1,https://twitter.com/dog_rates/status/820749716845686786/photo/12
 
0.1%
https://vine.co/v/ea0OwvPTx9l2
 
0.1%
https://twitter.com/dog_rates/status/837820167694528512/photo/1,https://twitter.com/dog_rates/status/837820167694528512/photo/12
 
0.1%
https://twitter.com/dog_rates/status/786233965241827333/photo/12
 
0.1%
Other values (1768)1829
96.7%
(Missing)43
 
2.3%

Length

2022-09-15T00:09:20.697928image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
https://twitter.com/dog_rates/status/819227688460238848/photo/12
 
0.1%
https://www.gofundme.com/lolas-life-saving-surgery-funds,https://twitter.com/dog_rates/status/835264098648616962/photo/1,https://twitter.com/dog_rates/status/835264098648616962/photo/12
 
0.1%
https://twitter.com/dog_rates/status/842163532590374912/photo/1,https://twitter.com/dog_rates/status/842163532590374912/photo/12
 
0.1%
https://twitter.com/dog_rates/status/810254108431155201/photo/12
 
0.1%
http://www.gofundme.com/bluethewhitehusky,https://twitter.com/dog_rates/status/831650051525054464/photo/1,https://twitter.com/dog_rates/status/831650051525054464/photo/1,https://twitter.com/dog_rates/status/831650051525054464/photo/1,https://twitter.com/dog_rates/status/831650051525054464/photo/12
 
0.1%
https://twitter.com/dog_rates/status/809220051211603969/photo/1,https://twitter.com/dog_rates/status/809220051211603969/photo/12
 
0.1%
https://twitter.com/dog_rates/status/759923798737051648/photo/12
 
0.1%
https://twitter.com/dog_rates/status/739979191639244800/photo/12
 
0.1%
https://twitter.com/dog_rates/status/833124694597443584/photo/1,https://twitter.com/dog_rates/status/833124694597443584/photo/1,https://twitter.com/dog_rates/status/833124694597443584/photo/12
 
0.1%
https://twitter.com/dog_rates/status/700143752053182464/photo/12
 
0.1%
Other values (1768)1829
98.9%

Most occurring characters

ValueCountFrequency (%)
t24927
 
13.9%
/19714
 
11.0%
s11166
 
6.2%
o11035
 
6.2%
17108
 
4.0%
86165
 
3.4%
e5852
 
3.3%
75734
 
3.2%
h5615
 
3.1%
a5602
 
3.1%
Other values (62)76351
42.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter96852
54.0%
Decimal Number52417
29.2%
Other Punctuation26615
 
14.8%
Connector Punctuation2684
 
1.5%
Uppercase Letter605
 
0.3%
Dash Punctuation87
 
< 0.1%
Math Symbol9
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t24927
25.7%
s11166
11.5%
o11035
11.4%
e5852
 
6.0%
h5615
 
5.8%
a5602
 
5.8%
p5602
 
5.8%
r5565
 
5.7%
i3115
 
3.2%
c2959
 
3.1%
Other values (16)15414
15.9%
Uppercase Letter
ValueCountFrequency (%)
E38
 
6.3%
M38
 
6.3%
X35
 
5.8%
O33
 
5.5%
F33
 
5.5%
Y33
 
5.5%
K32
 
5.3%
L31
 
5.1%
D30
 
5.0%
W30
 
5.0%
Other values (16)272
45.0%
Decimal Number
ValueCountFrequency (%)
17108
13.6%
86165
11.8%
75734
10.9%
65567
10.6%
05262
10.0%
44577
8.7%
24539
8.7%
34494
8.6%
54492
8.6%
94479
8.5%
Other Punctuation
ValueCountFrequency (%)
/19714
74.1%
.2944
 
11.1%
:2896
 
10.9%
,1047
 
3.9%
?6
 
< 0.1%
&4
 
< 0.1%
%4
 
< 0.1%
Connector Punctuation
ValueCountFrequency (%)
_2684
100.0%
Dash Punctuation
ValueCountFrequency (%)
-87
100.0%
Math Symbol
ValueCountFrequency (%)
=9
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin97457
54.4%
Common81812
45.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
t24927
25.6%
s11166
11.5%
o11035
11.3%
e5852
 
6.0%
h5615
 
5.8%
a5602
 
5.7%
p5602
 
5.7%
r5565
 
5.7%
i3115
 
3.2%
c2959
 
3.0%
Other values (42)16019
16.4%
Common
ValueCountFrequency (%)
/19714
24.1%
17108
 
8.7%
86165
 
7.5%
75734
 
7.0%
65567
 
6.8%
05262
 
6.4%
44577
 
5.6%
24539
 
5.5%
34494
 
5.5%
54492
 
5.5%
Other values (10)14160
17.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII179269
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t24927
 
13.9%
/19714
 
11.0%
s11166
 
6.2%
o11035
 
6.2%
17108
 
4.0%
86165
 
3.4%
e5852
 
3.3%
75734
 
3.2%
h5615
 
3.1%
a5602
 
3.1%
Other values (62)76351
42.6%

rating_numerator
Real number (ℝ≥0)

HIGH CORRELATION

Distinct10
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.59778013
Minimum10
Maximum75
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size29.6 KiB
2022-09-15T00:09:20.949906image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile10
Q111
median12
Q312
95-th percentile13
Maximum75
Range65
Interquartile range (IQR)1

Descriptive statistics

Standard deviation2.408440854
Coefficient of variation (CV)0.2076639519
Kurtosis508.2309534
Mean11.59778013
Median Absolute Deviation (MAD)1
Skewness19.56729611
Sum21943
Variance5.800587349
MonotonicityNot monotonic
2022-09-15T00:09:21.276512image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
12557
29.4%
11463
24.5%
10461
24.4%
13350
18.5%
1454
 
2.9%
152
 
0.1%
752
 
0.1%
171
 
0.1%
271
 
0.1%
261
 
0.1%
ValueCountFrequency (%)
10461
24.4%
11463
24.5%
12557
29.4%
13350
18.5%
1454
 
2.9%
152
 
0.1%
171
 
0.1%
261
 
0.1%
271
 
0.1%
752
 
0.1%
ValueCountFrequency (%)
752
 
0.1%
271
 
0.1%
261
 
0.1%
171
 
0.1%
152
 
0.1%
1454
 
2.9%
13350
18.5%
12557
29.4%
11463
24.5%
10461
24.4%

rating_denominator
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size29.6 KiB
10
1892 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters3784
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row10
2nd row10
3rd row10
4th row10
5th row10

Common Values

ValueCountFrequency (%)
101892
100.0%

Length

2022-09-15T00:09:21.591445image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-15T00:09:21.893852image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
101892
100.0%

Most occurring characters

ValueCountFrequency (%)
11892
50.0%
01892
50.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number3784
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
11892
50.0%
01892
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common3784
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
11892
50.0%
01892
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII3784
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
11892
50.0%
01892
50.0%

name
Categorical

HIGH CARDINALITY
MISSING

Distinct812
Distinct (%)43.8%
Missing37
Missing (%)2.0%
Memory size29.6 KiB
None
583 
Charlie
 
12
Cooper
 
10
Lucy
 
10
Oliver
 
10
Other values (807)
1230 

Length

Max length14
Median length11
Mean length4.939083558
Min length1

Characters and Unicode

Total characters9162
Distinct characters55
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique575 ?
Unique (%)31.0%

Sample

1st rowPhineas
2nd rowTilly
3rd rowArchie
4th rowDarla
5th rowFranklin

Common Values

ValueCountFrequency (%)
None583
30.8%
Charlie12
 
0.6%
Cooper10
 
0.5%
Lucy10
 
0.5%
Oliver10
 
0.5%
Tucker9
 
0.5%
Bo9
 
0.5%
Lola8
 
0.4%
Winston8
 
0.4%
Penny8
 
0.4%
Other values (802)1188
62.8%
(Missing)37
 
2.0%

Length

2022-09-15T00:09:22.179668image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
none583
31.4%
charlie12
 
0.6%
cooper10
 
0.5%
lucy10
 
0.5%
oliver10
 
0.5%
tucker9
 
0.5%
bo9
 
0.5%
lola8
 
0.4%
winston8
 
0.4%
penny8
 
0.4%
Other values (802)1188
64.0%

Most occurring characters

ValueCountFrequency (%)
e1336
14.6%
o1059
 
11.6%
n960
 
10.5%
N602
 
6.6%
a538
 
5.9%
i472
 
5.2%
l414
 
4.5%
r404
 
4.4%
y275
 
3.0%
s243
 
2.7%
Other values (45)2859
31.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter7334
80.0%
Uppercase Letter1828
 
20.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e1336
18.2%
o1059
14.4%
n960
13.1%
a538
 
7.3%
i472
 
6.4%
l414
 
5.6%
r404
 
5.5%
y275
 
3.7%
s243
 
3.3%
u234
 
3.2%
Other values (20)1399
19.1%
Uppercase Letter
ValueCountFrequency (%)
N602
32.9%
S129
 
7.1%
B119
 
6.5%
C114
 
6.2%
L95
 
5.2%
R85
 
4.6%
M82
 
4.5%
T64
 
3.5%
D63
 
3.4%
A60
 
3.3%
Other values (15)415
22.7%

Most occurring scripts

ValueCountFrequency (%)
Latin9162
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e1336
14.6%
o1059
 
11.6%
n960
 
10.5%
N602
 
6.6%
a538
 
5.9%
i472
 
5.2%
l414
 
4.5%
r404
 
4.4%
y275
 
3.0%
s243
 
2.7%
Other values (45)2859
31.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII9154
99.9%
None8
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e1336
14.6%
o1059
 
11.6%
n960
 
10.5%
N602
 
6.6%
a538
 
5.9%
i472
 
5.2%
l414
 
4.5%
r404
 
4.4%
y275
 
3.0%
s243
 
2.7%
Other values (41)2851
31.1%
None
ValueCountFrequency (%)
é4
50.0%
á2
25.0%
ò1
 
12.5%
ó1
 
12.5%

stage
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size16.8 KiB
None
1561 
pupper
213 
doggo
 
79
puppo
 
29
floofer
 
10

Length

Max length7
Median length4
Mean length4.298097252
Min length4

Characters and Unicode

Total characters8132
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNone
2nd rowNone
3rd rowNone
4th rowNone
5th rowNone

Common Values

ValueCountFrequency (%)
None1561
82.5%
pupper213
 
11.3%
doggo79
 
4.2%
puppo29
 
1.5%
floofer10
 
0.5%

Length

2022-09-15T00:09:22.526794image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-15T00:09:23.288869image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
none1561
82.5%
pupper213
 
11.3%
doggo79
 
4.2%
puppo29
 
1.5%
floofer10
 
0.5%

Most occurring characters

ValueCountFrequency (%)
e1784
21.9%
o1768
21.7%
N1561
19.2%
n1561
19.2%
p726
8.9%
u242
 
3.0%
r223
 
2.7%
g158
 
1.9%
d79
 
1.0%
f20
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter6571
80.8%
Uppercase Letter1561
 
19.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e1784
27.1%
o1768
26.9%
n1561
23.8%
p726
11.0%
u242
 
3.7%
r223
 
3.4%
g158
 
2.4%
d79
 
1.2%
f20
 
0.3%
l10
 
0.2%
Uppercase Letter
ValueCountFrequency (%)
N1561
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin8132
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e1784
21.9%
o1768
21.7%
N1561
19.2%
n1561
19.2%
p726
8.9%
u242
 
3.0%
r223
 
2.7%
g158
 
1.9%
d79
 
1.0%
f20
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII8132
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e1784
21.9%
o1768
21.7%
N1561
19.2%
n1561
19.2%
p726
8.9%
u242
 
3.0%
r223
 
2.7%
g158
 
1.9%
d79
 
1.0%
f20
 
0.2%

favourites_count
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size29.6 KiB
114031
1877 
114032
 
15

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters11352
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row114031
2nd row114031
3rd row114031
4th row114031
5th row114031

Common Values

ValueCountFrequency (%)
1140311877
99.2%
11403215
 
0.8%

Length

2022-09-15T00:09:23.554007image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-15T00:09:23.800794image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
1140311877
99.2%
11403215
 
0.8%

Most occurring characters

ValueCountFrequency (%)
15661
49.9%
41892
 
16.7%
01892
 
16.7%
31892
 
16.7%
215
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number11352
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
15661
49.9%
41892
 
16.7%
01892
 
16.7%
31892
 
16.7%
215
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common11352
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
15661
49.9%
41892
 
16.7%
01892
 
16.7%
31892
 
16.7%
215
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII11352
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
15661
49.9%
41892
 
16.7%
01892
 
16.7%
31892
 
16.7%
215
 
0.1%

retweet_count
Real number (ℝ≥0)

HIGH CORRELATION

Distinct1484
Distinct (%)78.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3667.895349
Minimum0
Maximum79515
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size29.6 KiB
2022-09-15T00:09:24.121879image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile141.1
Q1771.75
median1923
Q34194.25
95-th percentile12165.7
Maximum79515
Range79515
Interquartile range (IQR)3422.5

Descriptive statistics

Standard deviation5739.243632
Coefficient of variation (CV)1.564723932
Kurtosis38.50815817
Mean3667.895349
Median Absolute Deviation (MAD)1373
Skewness5.025032153
Sum6939658
Variance32938917.47
MonotonicityNot monotonic
2022-09-15T00:09:24.535027image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19725
 
0.3%
36525
 
0.3%
834
 
0.2%
5774
 
0.2%
1154
 
0.2%
12074
 
0.2%
32613
 
0.2%
7483
 
0.2%
12313
 
0.2%
1793
 
0.2%
Other values (1474)1854
98.0%
ValueCountFrequency (%)
01
0.1%
21
0.1%
32
0.1%
41
0.1%
61
0.1%
71
0.1%
102
0.1%
141
0.1%
161
0.1%
171
0.1%
ValueCountFrequency (%)
795151
0.1%
566252
0.1%
523602
0.1%
482651
0.1%
458491
0.1%
422282
0.1%
379112
0.1%
334212
0.1%
328832
0.1%
319891
0.1%

t_link
Categorical

HIGH CARDINALITY

Distinct1578
Distinct (%)83.4%
Missing0
Missing (%)0.0%
Memory size29.6 KiB
NaN
250 
https://t.co/zrMVdfFej6
 
2
https://t.co/Bb3xnpsWBC
 
2
https://t.co/y5KarNXWXt
 
2
https://t.co/liJGwMp17E
 
2
Other values (1573)
1634 

Length

Max length23
Median length23
Mean length20.35729387
Min length3

Characters and Unicode

Total characters38516
Distinct characters65
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1512 ?
Unique (%)79.9%

Sample

1st rowhttps://t.co/MgUWQ76dJU
2nd rowhttps://t.co/0Xxu71qeIV
3rd rowhttps://t.co/wUnZnhtVJB
4th rowhttps://t.co/tD36da7qLQ
5th rowhttps://t.co/AtUZn91f7f

Common Values

ValueCountFrequency (%)
NaN250
 
13.2%
https://t.co/zrMVdfFej62
 
0.1%
https://t.co/Bb3xnpsWBC2
 
0.1%
https://t.co/y5KarNXWXt2
 
0.1%
https://t.co/liJGwMp17E2
 
0.1%
https://t.co/ghyT4Ubk1r2
 
0.1%
https://t.co/QV5nx6otZR2
 
0.1%
https://t.co/Ln89R4FP7v2
 
0.1%
https://t.co/lprdOylVpS2
 
0.1%
https://t.co/rLi83ZyCL52
 
0.1%
Other values (1568)1624
85.8%

Length

2022-09-15T00:09:25.226682image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
nan250
 
13.2%
https://t.co/o5j479bzuc2
 
0.1%
https://t.co/mimktsln6k2
 
0.1%
https://t.co/tu4szogviq2
 
0.1%
https://t.co/mh0ioyfdig2
 
0.1%
https://t.co/hyac5hq9gc2
 
0.1%
https://t.co/jkxh0nbbnl2
 
0.1%
https://t.co/suxci9b7pq2
 
0.1%
https://t.co/r6wjyc2tey2
 
0.1%
https://t.co/dkbyacag2d2
 
0.1%
Other values (1568)1624
85.8%

Most occurring characters

ValueCountFrequency (%)
t5164
 
13.4%
/4926
 
12.8%
c1897
 
4.9%
o1897
 
4.9%
p1893
 
4.9%
h1885
 
4.9%
s1878
 
4.9%
:1642
 
4.3%
.1642
 
4.3%
N772
 
2.0%
Other values (55)14920
38.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter20124
52.2%
Other Punctuation8210
21.3%
Uppercase Letter7574
 
19.7%
Decimal Number2608
 
6.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t5164
25.7%
c1897
 
9.4%
o1897
 
9.4%
p1893
 
9.4%
h1885
 
9.4%
s1878
 
9.3%
a510
 
2.5%
d301
 
1.5%
u301
 
1.5%
r299
 
1.5%
Other values (16)4099
20.4%
Uppercase Letter
ValueCountFrequency (%)
N772
 
10.2%
A311
 
4.1%
I311
 
4.1%
X300
 
4.0%
H290
 
3.8%
B289
 
3.8%
Q283
 
3.7%
E282
 
3.7%
C279
 
3.7%
J278
 
3.7%
Other values (16)4179
55.2%
Decimal Number
ValueCountFrequency (%)
6282
10.8%
7280
10.7%
5272
10.4%
0272
10.4%
1256
9.8%
4256
9.8%
9255
9.8%
8254
9.7%
3243
9.3%
2238
9.1%
Other Punctuation
ValueCountFrequency (%)
/4926
60.0%
:1642
 
20.0%
.1642
 
20.0%

Most occurring scripts

ValueCountFrequency (%)
Latin27698
71.9%
Common10818
 
28.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
t5164
18.6%
c1897
 
6.8%
o1897
 
6.8%
p1893
 
6.8%
h1885
 
6.8%
s1878
 
6.8%
N772
 
2.8%
a510
 
1.8%
A311
 
1.1%
I311
 
1.1%
Other values (42)11180
40.4%
Common
ValueCountFrequency (%)
/4926
45.5%
:1642
 
15.2%
.1642
 
15.2%
6282
 
2.6%
7280
 
2.6%
5272
 
2.5%
0272
 
2.5%
1256
 
2.4%
4256
 
2.4%
9255
 
2.4%
Other values (3)735
 
6.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII38516
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t5164
 
13.4%
/4926
 
12.8%
c1897
 
4.9%
o1897
 
4.9%
p1893
 
4.9%
h1885
 
4.9%
s1878
 
4.9%
:1642
 
4.3%
.1642
 
4.3%
N772
 
2.0%
Other values (55)14920
38.7%

month
Real number (ℝ≥0)

HIGH CORRELATION

Distinct12
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.67230444
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size29.6 KiB
2022-09-15T00:09:25.797172image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median7
Q311
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)8

Descriptive statistics

Standard deviation3.990657304
Coefficient of variation (CV)0.5980928089
Kurtosis-1.497672987
Mean6.67230444
Median Absolute Deviation (MAD)4
Skewness-0.0007991162542
Sum12624
Variance15.92534572
MonotonicityNot monotonic
2022-09-15T00:09:26.207067image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
12314
16.6%
11251
13.3%
1235
12.4%
3179
9.5%
2176
9.3%
7146
7.7%
6137
7.2%
4109
 
5.8%
5108
 
5.7%
1088
 
4.7%
Other values (2)149
7.9%
ValueCountFrequency (%)
1235
12.4%
2176
9.3%
3179
9.5%
4109
5.8%
5108
5.7%
6137
7.2%
7146
7.7%
870
 
3.7%
979
 
4.2%
1088
 
4.7%
ValueCountFrequency (%)
12314
16.6%
11251
13.3%
1088
 
4.7%
979
 
4.2%
870
 
3.7%
7146
7.7%
6137
7.2%
5108
 
5.7%
4109
 
5.8%
3179
9.5%

week
Real number (ℝ≥0)

ZEROS

Distinct7
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.839852008
Minimum0
Maximum6
Zeros303
Zeros (%)16.0%
Negative0
Negative (%)0.0%
Memory size29.6 KiB
2022-09-15T00:09:26.568904image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q35
95-th percentile6
Maximum6
Range6
Interquartile range (IQR)4

Descriptive statistics

Standard deviation1.983868732
Coefficient of variation (CV)0.698581731
Kurtosis-1.229808596
Mean2.839852008
Median Absolute Deviation (MAD)2
Skewness0.08532368946
Sum5373
Variance3.935735145
MonotonicityNot monotonic
2022-09-15T00:09:26.799660image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0303
16.0%
1288
15.2%
2279
14.7%
4270
14.3%
3270
14.3%
5255
13.5%
6227
12.0%
ValueCountFrequency (%)
0303
16.0%
1288
15.2%
2279
14.7%
3270
14.3%
4270
14.3%
5255
13.5%
6227
12.0%
ValueCountFrequency (%)
6227
12.0%
5255
13.5%
4270
14.3%
3270
14.3%
2279
14.7%
1288
15.2%
0303
16.0%

day_of_the year
Real number (ℝ≥0)

HIGH CORRELATION

Distinct362
Distinct (%)19.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean188.096723
Minimum1
Maximum366
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size29.6 KiB
2022-09-15T00:09:27.138652image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile12
Q170
median183
Q3324
95-th percentile354
Maximum366
Range365
Interquartile range (IQR)254

Descriptive statistics

Standard deviation122.4755478
Coefficient of variation (CV)0.6511306833
Kurtosis-1.505204987
Mean188.096723
Median Absolute Deviation (MAD)126
Skewness0.002206727895
Sum355879
Variance15000.25981
MonotonicityNot monotonic
2022-09-15T00:09:27.545808image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
32720
 
1.1%
34118
 
1.0%
34218
 
1.0%
33218
 
1.0%
34418
 
1.0%
34616
 
0.8%
32316
 
0.8%
32616
 
0.8%
33316
 
0.8%
33516
 
0.8%
Other values (352)1720
90.9%
ValueCountFrequency (%)
14
 
0.2%
210
0.5%
37
0.4%
48
0.4%
59
0.5%
69
0.5%
79
0.5%
88
0.4%
97
0.4%
107
0.4%
ValueCountFrequency (%)
3661
 
0.1%
3655
 
0.3%
3646
0.3%
3637
0.4%
3627
0.4%
3619
0.5%
36011
0.6%
35913
0.7%
35811
0.6%
3575
 
0.3%

year
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size29.6 KiB
2016
1010 
2017
471 
2015
411 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters7568
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2017
2nd row2017
3rd row2017
4th row2017
5th row2017

Common Values

ValueCountFrequency (%)
20161010
53.4%
2017471
24.9%
2015411
21.7%

Length

2022-09-15T00:09:28.039221image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-15T00:09:28.340993image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
20161010
53.4%
2017471
24.9%
2015411
21.7%

Most occurring characters

ValueCountFrequency (%)
21892
25.0%
01892
25.0%
11892
25.0%
61010
13.3%
7471
 
6.2%
5411
 
5.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number7568
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
21892
25.0%
01892
25.0%
11892
25.0%
61010
13.3%
7471
 
6.2%
5411
 
5.4%

Most occurring scripts

ValueCountFrequency (%)
Common7568
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
21892
25.0%
01892
25.0%
11892
25.0%
61010
13.3%
7471
 
6.2%
5411
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII7568
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
21892
25.0%
01892
25.0%
11892
25.0%
61010
13.3%
7471
 
6.2%
5411
 
5.4%

Interactions

2022-09-15T00:09:08.001124image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-15T00:08:41.631282image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-15T00:08:44.730643image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-15T00:08:47.222825image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-15T00:08:51.258115image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-15T00:08:53.858968image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-15T00:08:56.446599image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-15T00:08:59.258934image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-15T00:09:01.987621image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-15T00:09:08.638983image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-15T00:08:42.083982image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-15T00:08:44.970793image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-15T00:08:47.500763image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-15T00:08:51.554088image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-15T00:08:54.156988image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-15T00:08:56.742923image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-15T00:08:59.515939image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-15T00:09:02.368699image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-15T00:09:09.111957image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-15T00:08:42.378844image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-15T00:08:45.179740image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-15T00:08:47.773141image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-15T00:08:51.881129image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-15T00:08:54.415777image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-15T00:08:56.978765image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-15T00:08:59.761350image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-15T00:09:02.648857image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-15T00:09:09.615595image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-15T00:08:42.718846image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-15T00:08:45.434218image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-15T00:08:48.022355image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-15T00:08:52.160893image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-15T00:08:54.771996image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-15T00:08:57.228215image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-15T00:09:00.015370image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-15T00:09:03.399375image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-15T00:09:10.209570image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-15T00:08:43.064766image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-15T00:08:45.760946image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-15T00:08:48.283176image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-15T00:08:52.436100image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-15T00:08:55.092746image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-15T00:08:57.491737image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-15T00:09:00.341245image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-15T00:09:03.908472image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-15T00:09:10.624856image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-15T00:08:43.421893image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-15T00:08:46.063818image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-15T00:08:49.879843image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-15T00:08:52.778055image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-15T00:08:55.355031image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-15T00:08:57.977033image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-15T00:09:00.706809image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-15T00:09:04.818153image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-15T00:09:10.925106image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-15T00:08:43.771834image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-15T00:08:46.316278image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-15T00:08:50.247832image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-15T00:08:53.055063image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-15T00:08:55.643955image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-15T00:08:58.349016image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-15T00:09:00.952628image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-15T00:09:06.107818image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-15T00:09:11.231988image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-15T00:08:44.083966image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-15T00:08:46.582516image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-15T00:08:50.595122image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-15T00:08:53.313023image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-15T00:08:55.897380image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-15T00:08:58.627778image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-15T00:09:01.232775image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-15T00:09:06.666929image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-15T00:09:11.627994image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-15T00:08:44.393906image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-15T00:08:46.876790image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-15T00:08:50.888270image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-15T00:08:53.585911image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-15T00:08:56.163080image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-15T00:08:58.904089image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-15T00:09:01.505049image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-15T00:09:07.211891image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Correlations

2022-09-15T00:09:28.588225image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-09-15T00:09:29.056985image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-09-15T00:09:29.528346image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-09-15T00:09:29.993984image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-09-15T00:09:30.410326image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-09-15T00:09:12.445041image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
A simple visualization of nullity by column.
2022-09-15T00:09:13.680919image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-09-15T00:09:14.332253image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-09-15T00:09:14.532828image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

idin_reply_to_status_idin_reply_to_user_idtimestampsourcetextretweeted_status_idexpanded_urlsrating_numeratorrating_denominatornamestagefavourites_countretweet_countt_linkmonthweekday_of_the yearyear
0892420643555336193NaNNaN2017-08-01 16:23:56+00:00iPhoneThis is Phineas. He's a mystical boy. Only ever appears in the hole of a donut. 13/10 https://t.co/MgUWQ76dJUNaNhttps://twitter.com/dog_rates/status/892420643555336193/photo/11310PhineasNone1140318853https://t.co/MgUWQ76dJU812132017
1892177421306343426NaNNaN2017-08-01 00:17:27+00:00iPhoneThis is Tilly. She's just checking pup on you. Hopes you're doing ok. If not, she's available for pats, snugs, boops, the whole bit. 13/10 https://t.co/0Xxu71qeIVNaNhttps://twitter.com/dog_rates/status/892177421306343426/photo/11310TillyNone1140316514https://t.co/0Xxu71qeIV812132017
2891815181378084864NaNNaN2017-07-31 00:18:03+00:00iPhoneThis is Archie. He is a rare Norwegian Pouncing Corgo. Lives in the tall grass. You never know when one may strike. 12/10 https://t.co/wUnZnhtVJBNaNhttps://twitter.com/dog_rates/status/891815181378084864/photo/11210ArchieNone1140314328https://t.co/wUnZnhtVJB702122017
3891689557279858688NaNNaN2017-07-30 15:58:51+00:00iPhoneThis is Darla. She commenced a snooze mid meal. 13/10 happens to the best of us https://t.co/tD36da7qLQNaNhttps://twitter.com/dog_rates/status/891689557279858688/photo/11310DarlaNone1140318964https://t.co/tD36da7qLQ762112017
4891327558926688256NaNNaN2017-07-29 16:00:24+00:00iPhoneThis is Franklin. He would like you to stop calling him "cute." He is a very fierce shark and should be respected as such. 12/10 #BarkWeek https://t.co/AtUZn91f7fNaNhttps://twitter.com/dog_rates/status/891327558926688256/photo/1,https://twitter.com/dog_rates/status/891327558926688256/photo/11210FranklinNone1140319774https://t.co/AtUZn91f7f752102017
5891087950875897856NaNNaN2017-07-29 00:08:17+00:00iPhoneHere we have a majestic great white breaching off South Africa's coast. Absolutely h*ckin breathtaking. 13/10 (IG: tucker_marlo) #BarkWeek https://t.co/kQ04fDDRmhNaNhttps://twitter.com/dog_rates/status/891087950875897856/photo/11310NoneNone1140313261https://t.co/kQ04fDDRmh752102017
6890971913173991426NaNNaN2017-07-28 16:27:12+00:00iPhoneMeet Jax. He enjoys ice cream so much he gets nervous around it. 13/10 help Jax enjoy more things by clicking below\n\nhttps://t.co/Zr4hWfAs1H https://t.co/tVJBRMnhxlNaNhttps://gofundme.com/ydvmve-surgery-for-jax,https://twitter.com/dog_rates/status/890971913173991426/photo/11310JaxNone1140312158https://t.co/tVJBRMnhxl742092017
7890729181411237888NaNNaN2017-07-28 00:22:40+00:00iPhoneWhen you watch your owner call another dog a good boy but then they turn back to you and say you're a great boy. 13/10 https://t.co/v0nONBcwxqNaNhttps://twitter.com/dog_rates/status/890729181411237888/photo/1,https://twitter.com/dog_rates/status/890729181411237888/photo/11310NoneNone11403116716https://t.co/v0nONBcwxq742092017
8890609185150312448NaNNaN2017-07-27 16:25:51+00:00iPhoneThis is Zoey. She doesn't want to be one of the scary sharks. Just wants to be a snuggly pettable boatpet. 13/10 #BarkWeek https://t.co/9TwLuAGH0bNaNhttps://twitter.com/dog_rates/status/890609185150312448/photo/11310ZoeyNone1140314429https://t.co/9TwLuAGH0b732082017
9890240255349198849NaNNaN2017-07-26 15:59:51+00:00iPhoneThis is Cassie. She is a college pup. Studying international doggo communication and stick theory. 14/10 so elegant much sophisticate https://t.co/t1bfwz5S2ANaNhttps://twitter.com/dog_rates/status/890240255349198849/photo/11410Cassiedoggo1140317711https://t.co/t1bfwz5S2A722072017

Last rows

idin_reply_to_status_idin_reply_to_user_idtimestampsourcetextretweeted_status_idexpanded_urlsrating_numeratorrating_denominatornamestagefavourites_countretweet_countt_linkmonthweekday_of_the yearyear
1882666418789513326592NaNNaN2015-11-17 00:53:15+00:00iPhoneThis is Walter. He is an Alaskan Terrapin. Loves outdated bandanas. One ear still working. Cool house plant. 10/10 https://t.co/qXpcwENTvnNaNhttps://twitter.com/dog_rates/status/666418789513326592/photo/11010WalterNone11403148https://t.co/qXpcwENTvn1113212015
1883666373753744588802NaNNaN2015-11-16 21:54:18+00:00iPhoneThose are sunglasses and a jean jacket. 11/10 dog cool af https://t.co/uHXrPkUEylNaNhttps://twitter.com/dog_rates/status/666373753744588802/photo/11110NoneNone114031100https://t.co/uHXrPkUEyl1103202015
1884666345417576210432NaNNaN2015-11-16 20:01:42+00:00iPhoneLook at this jokester thinking seat belt laws don't apply to him. Great tongue tho 10/10 https://t.co/VFKG1vxGjBNaNhttps://twitter.com/dog_rates/status/666345417576210432/photo/11010NoneNone114031146https://t.co/VFKG1vxGjB1103202015
1885666273097616637952NaNNaN2015-11-16 15:14:19+00:00iPhoneCan take selfies 11/10 https://t.co/ws2AMaNwPWNaNhttps://twitter.com/dog_rates/status/666273097616637952/photo/11110NoneNone11403182https://t.co/ws2AMaNwPW1103202015
1886666268910803644416NaNNaN2015-11-16 14:57:41+00:00iPhoneVery concerned about fellow dog trapped in computer. 10/10 https://t.co/0yxApIikpkNaNhttps://twitter.com/dog_rates/status/666268910803644416/photo/11010NoneNone11403137https://t.co/0yxApIikpk1103202015
1887666102155909144576NaNNaN2015-11-16 03:55:04+00:00iPhoneOh my. Here you are seeing an Adobe Setter giving birth to twins!!! The world is an amazing place. 11/10 https://t.co/11LvqN4WLqNaNhttps://twitter.com/dog_rates/status/666102155909144576/photo/11110NoneNone11403116https://t.co/11LvqN4WLq1103202015
1888666073100786774016NaNNaN2015-11-16 01:59:36+00:00iPhoneLet's hope this flight isn't Malaysian (lol). What a dog! Almost completely camouflaged. 10/10 I trust this pilot https://t.co/Yk6GHE9tOYNaNhttps://twitter.com/dog_rates/status/666073100786774016/photo/11010NoneNone114031174https://t.co/Yk6GHE9tOY1103202015
1889666063827256086533NaNNaN2015-11-16 01:22:45+00:00iPhoneThis is the happiest dog you will ever see. Very committed owner. Nice couch. 10/10 https://t.co/RhUEAloehKNaNhttps://twitter.com/dog_rates/status/666063827256086533/photo/11010NoneNone114031232https://t.co/RhUEAloehK1103202015
1890666055525042405380NaNNaN2015-11-16 00:49:46+00:00iPhoneHere is a Siberian heavily armored polar bear mix. Strong owner. 10/10 I would do unspeakable things to pet this dog https://t.co/rdivxLiqEtNaNhttps://twitter.com/dog_rates/status/666055525042405380/photo/11010NoneNone114031261https://t.co/rdivxLiqEt1103202015
1891666050758794694657NaNNaN2015-11-16 00:30:50+00:00iPhoneThis is a truly beautiful English Wilson Staff retriever. Has a nice phone. Privileged. 10/10 would trade lives with https://t.co/fvIbQfHjIeNaNhttps://twitter.com/dog_rates/status/666050758794694657/photo/11010NoneNone11403160https://t.co/fvIbQfHjIe1103202015